EvolGAN: Evolutionary Generative Adversarial Networks
| Autor: | Fabien Teytaud, Jeremy Rapin, Hanhe Lin, Baptiste Roziere, Vlad Hosu, Olivier Teytaud, Mariia Zameshina |
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| Přispěvatelé: | Facebook AI Research [Paris] (FAIR), Facebook, Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Limnological Institute, University of Konstanz, Konstanz, Germany, Université Grenoble Alpes - UFR Informatique et Mathématiques Appliquées (UGA UFR IMAG), Université Grenoble Alpes (UGA), Teytaud, Fabien |
| Rok vydání: | 2021 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Space (commercial competition) 01 natural sciences Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Image (mathematics) Adversarial system [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 0202 electrical engineering electronic engineering information engineering Quality (business) ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences media_common business.industry Estimator [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] 020201 artificial intelligence & image processing Artificial intelligence business Generative grammar Generator (mathematics) |
| Zdroj: | Computer Vision – ACCV 2020 ISBN: 9783030695378 ACCV (4) Asia Conference on Computer Vision (ACCV) Asia Conference on Computer Vision (ACCV), Nov 2020, Virtual, Japan |
| Popis: | We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new method leads to the generation of significantly higher quality images while preserving the original generator's diversity. Human raters preferred an image from the new version with frequency 83.7pc for Cats, 74pc for FashionGen, 70.4pc for Horses, and 69.2pc for Artworks, and minor improvements for the already excellent GANs for faces. This approach applies to any quality scorer and GAN generator. accepted ACCV oral |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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